Target concentration estimation in hyperspectral imagery

Chanel Michaeli; Noam Atias; Stanley R. Rotman; Haim Elisha

doi:10.1117/12.3031142

1 November 2024 Target concentration estimation in hyperspectral imagery

Chanel Michaeli, Noam Atias, Stanley R. Rotman, Haim Elisha

Author Affiliations +

Proceedings Volume 13200, Electro-Optical and Infrared Systems: Technology and Applications XXI; 132001H (2024) https://doi.org/10.1117/12.3031142
Event: Security + Defence, 2024, Edinburgh, United Kingdom

Abstract

This technical review presents a novel algorithm for target concentration estimation in hyperspectral imaging, comparing its performance with an existing method. Using the substitutive model of the matched filter for target detection, evaluation of both algorithms was based on target detection accuracy and false positive rates. Our findings reveal that while the new algorithm offers more accurate mean estimation of target concentration, the existing algorithm exhibits lower variance and superior detection capabilities. These insights highlight the trade-offs between mean accuracy, variance, and detection efficacy in hyperspectral target detection algorithms, advancing our understanding of their performance in practical applications.

Conference Presentation

Citation Download Citation

Chanel Michaeli, Noam Atias, Stanley R. Rotman, and Haim Elisha "Target concentration estimation in hyperspectral imagery", Proc. SPIE 13200, Electro-Optical and Infrared Systems: Technology and Applications XXI, 132001H (1 November 2024); https://doi.org/10.1117/12.3031142

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